In the past, several analysis methods have been used to evaluate target materials, such as biosamples, that can generate a plurality of signals, each associated with a respective individual constituent in a complex mixture comprising a plurality of constituents, some of which may have overlapping signal contributions. The overlapping signals can produce a composite spectrum that can be deconvolved to estimate or determine the amount and/or presence of selected individual or subsets of constituents in the complex mixture. Such analysis methods include, but are not limited to spectroscopy, chromatography, and the like.
In the past, NMR spectroscopic evaluations of in vitro biosamples have been used to identify the presence of and/or measure the concentration or amounts of selected constituents in a complex mixture, the constituents having associated chemical lineshapes and/or peaks in the obtained NMR signal. For example, U.S. Pat. No. 4,933,844, entitled Measurement of Blood Lipoprotein Constituents by Analysis of Data Acquired from an NMR Spectrometer to Otvos and U.S. Pat. No. 5,343,389, entitled Method and Apparatus for Measuring Classes and Subclasses of Lipoproteins, also to Otvos, describe NMR evaluation techniques that concurrently obtain and then measure a plurality of different lipoprotein constituents in an in vitro blood or plasma sample. See also, U.S. patent application Ser. No. 10/208,371, entitled Method Of Determining Presence And Concentration Of Lipoprotein X In Blood Plasma And Serum, the contents of the above patents and patent application are hereby incorporated by reference as if recited in full herein.
Generally described, to evaluate the lipoproteins in a blood plasma and/or serum sample, the amplitudes and/or lineshapes of a plurality of NMR spectroscopy derived signals within a chemical shift region of the NMR spectrum are deconvoluted from the composite signal or spectrum, each signal component so deconvolved being associated with respective lipoprotein subclass constituents of interest or selected related groupings of subclass constituents of interest in the sample. The values of at least one selected subclass constituent (or groupings of selected subclass constituents) are compared to predetermined test criteria to evaluate a patient's risk of having or developing coronary artery or heart disease. Similarly, NMR spectroscopy evaluation of lipoproteins have been proposed to evaluate a patient's risk of having or developing insulin resistance, Type-2 diabetes, or related disorders. See, U.S. patent application Ser. No. 09/550,359, entitled Methods and Computer Program Products for Determining Risk of Developing Type 2 Diabetes and Other Insulin Resistance Related Disorders, the contents of which are hereby incorporated by reference as if recited in full herein.
Referring to FIG. 1, it is noted that the constituents of certain subclasses of lipoproteins have overlapping signals. For example, low-density lipoprotein (“LDL”) constituent values, shown for clarity as only two (L2 and L5) LDL subclass constituent values, when presented on a spectrum graph of signal intensity versus ppm, can overlap considerably. The overlapping nature of the signals produces a regression matrix that is nearly singular. Unfortunately, in conventional statistical evaluation methods that can employ non-negative least squares techniques on nearly collinear data, the regression coefficients may be unstable and, hence, variable. See Myers, Raymond H., Classical and Modern Regression with Applications, (2d ed., Mass. PWS-Kent, 1990); Box et al., Statistics for Experimenters; An Introduction to Design, Data Analysis, and Model Building, (New York, Wiley, 1978). The potential instability in the regression coefficients can force the non-negative least squares analysis to set certain constituent coefficients to zero, although these constituents may be more correctly identified as small positive values when analyzed properly. Further, although conventional methods are thought to be adequate for many clinical or other applications, particularly in light of the margin of error introduced by other testing methodologies, the instability may impede the statistical robustness or reproducibility of certain measurement results.
In view of the foregoing, there remains a need to provide improved deconvolution methods.